MiniOntology Generat Or MOGO Thesis Proposal MiniOntology Generation

Mini-Ontology Generat. Or (MOGO) Thesis Proposal Mini-Ontology Generation from Canonicalized Tables Stephen Lynn Data Extraction Research Group Department of Computer Science Brigham Young University Supported by the

Mini-Ontology Generat. Or (MOGO) TANGO Overview TANGO: Table ANalysis for Generating Ontologies Project consists of the following three components: 1. Transform tables into a canonicalized form 2. Generate mini-ontologies 3. Merge into a growing ontology Thesis Proposal

Mini-Ontology Generat. Or (MOGO) Thesis Proposal Thesis Statement § Proposed Solution § Develop a tool to accurately generate mini-ontologies from canonicalized tables of data automatically, semiautomatically, or manually. § Evaluation § Evaluate accuracy of tool with respect to: concept/value recognition, relationship discovery, and constraint discovery.

Mini-Ontology Generat. Or (MOGO) Thesis Proposal Sample Input Region and State Information Location Northeast Delaware Maine Northwest Oregon Washington Sample Output Population (2000) 2, 122, 869 817, 376 1, 305, 493 9, 690, 665 3, 559, 547 6, 131, 118 Latitude Longitude 45 44 -90 -93 45 43 -120

Mini-Ontology Generat. Or (MOGO) Thesis Proposal Mini-Ontology Generat. Or (MOGO) § Concept/Value Recognition § Relationship Discovery § Constraint Discovery NOTE: MOGO implements a base set of algorithms for each step of the process and allows for runtime integration of new algorithms.

Mini-Ontology Generat. Or (MOGO) Thesis Proposal Concept/Value Recognition § Lexical Clues § Data value assignment § Labels as data values § Default § Classifies any unclassified elements according to simple heuristic. Concepts and Value Assignments Region State Population Latitude Longitude Northeast Northwest Delaware Maine Oregon Washington 2, 122, 869 817, 376 1, 305, 493 9, 690, 665 3, 559, 547 6, 131, 118 45 44 45 43 -90 -93 -120

Mini-Ontology Generat. Or (MOGO) Relationship Discovery § Dimension Tree Mappings § Lexical Clues § Generalization/Specialization § Aggregation § Data Frames § Ontology Fragment Merge Thesis Proposal

Mini-Ontology Generat. Or (MOGO) Thesis Proposal Constraint Discovery § § Generalization/Specialization Computed Values Functional Relationships Optional Participation Region and State Information Location Northeast Delaware Maine Northwest Oregon Washington Population (2000) 2, 122, 869 817, 376 1, 305, 493 9, 690, 665 3, 559, 547 6, 131, 118 Latitude Longitude 45 44 -90 -93 45 43 -120

Mini-Ontology Generat. Or (MOGO) Thesis Proposal Validation § Concept/Value Recognition § Correctly identified concepts § Missed concepts § False positives § Data values assignment § Relationship Discovery § Valid relationship sets § Invalid relationship sets § Missed relationship sets § Constraint Discovery § Valid constraints § Invalid constraints § Missed constraints Precision Concept Recognition Relationship Discovery Constraint Discovery Recall

Mini-Ontology Generat. Or (MOGO) Thesis Proposal Contribution § Tool to generate mini-ontologies § Assessment of accuracy of automatic generation